Classification day 2

What do you mean by generalization in terms of the SVM?

• How far the hyperplane is from the support vectors

• How accurately the SVM can predict outcomes for unseen data

• The threshold amount of error in an SVM

In SVM, when the C parameter is rising:

• Margin will be smaller

• Margin will be bigger

• C parameter not affects margin

The effectiveness of an SVM depends upon:

• Selection of Kernel

• Kernel Parameters

• Soft Margin Parameter C

• All

Support vectors are the data points that lie closest to the decision surface.

• TRUE

• FALSE

What would happen when you use very small C (C~0)?

• Misclassification would happen

• Data will be correctly classified

• Can’t say

• None of these

If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?

• Underfitting

• Nothing, the model is perfect

• Overfitting

In simple linear regression, if you change the input value by 1 then output value will be changed by:

• The slope parameter

• The intercept parameter

• None

• 1

The Kernel Trick in Support Vector Classification

• Mapping low dimensional data to high dimensional space

• Using for features selection

• None of these

Which of the following machine learning algorithm can be used for both classification and regression problems?

• K-NN

• SVM

• Logistic Regression 

Which of the following statements is true for k-NN classifiers?

• K is not hyperparameter of algorithm

• The decision boundary is smoother with smaller values of k

• The decision boundary is linear

• k-NN does not require an explicit training step